Photovoltaic (PV) energy production is supposed to hugely increase during current and next decades. PV plants of any extension, technology and power are supposed to be developed and monitored, with the awareness that monitoring phase lasts around 25 years. One of the major challenges of PV plant industry is to choose suitable sites for plant installation and optimal configuration to maximize energy production. In fact, not all places are suitable for solar energy generation, due to several factors, like environmental factors (e.g., solar radiation, wind, shading), technical factors (e.g., distance from the distribution line), economic factors (e.g., development of local economies), and social/political factors (e.g., public acceptance, protected areas). We propose to address the problem of solar PV site selection using a multi-criteria decision-making (MCDM) approach together with geographic information system (GIS) software to determine the most suitable area or alternative, both in case of rural environments and smart city installations. The resulting framework, highly-integrated with several EU sources and initiatives, can be used for crucial steps in PV plants lifecycle, from initial design to monitoring and maintenance.

RCC*-9 is a mereotopological qualitative spatial calculus for simple lines and regions. RCC*-9 can be easily expressed in other existing models for topological relations and thus can be viewed as a candidate for being a “bridge” model among various approaches. In this paper, we present a revised and extended version of RCC*-9, which can handle non-simple geometric features, such as multipolygons, multipolylines, and multipoints, and 3D features, such as polyhedrons and lower-dimensional features embedded in R3. We also run experiments to compute RCC*-9 relations among very large random datasets of spatial features to demonstrate the JEPD properties of the calculus and also to compute the composition tables for spatial reasoning with the calculus.

The adoption of Bluetooth beacon technology demonstrates a broad interest in indoor positioning technology because of its low cost and ease of use. Bluetooth beacons usually have an accuracy of fewer than 4 meters. The use of machine learning (ML) leads to results with greater accuracy compared to using traditional filtering methods. In this paper, we provide indoor localization based on Bluetooth beacons using several different ML techniques. We used ML algorithms to locate customers' devices in shopping malls. The extra-trees classifier and k-neighbors classifier found the device with greater than 90% accuracy. Other algorithms were able to determine the location with less accuracy. The results also showed that Bluetooth technology is a valid solution to find the data used to analyze the spatial-temporal behavior of individuals.

Bendehiba Dahmane, Brahim Lejdel, Eliseo Clementini, Fayssal Harrats, Sameh Nassar, Lahcene Hadj Abderrahmane Department of Technology, Faculty of Technology, El-Oued University, El-Oued, Algeria Department of Computer Sciences, Faculty of Exact Sciences, El-Oued University, El-Oued, Algeria University of L'Aquila, L'Aquila, Italy Department of Electronics, Faculty of Electrical Engineering, Science and Technology University (USTO-MB), Oran, Algeria Mobile Multi-Sensor Systems (MMSS) Research Group, Department of Geomatics Engineering, University of Calgary, Alberta, Canada Department of Space Instrumentation, Satellite Development Center (CDS), Space Agency (ASAL), Oran, Algeria

Intelligent technologies have advanced significantly over the past two decades and have integrated with cities to enhance citizen lives. Additionally, the amount of energy consumed varies depending on the weather, the number of occupants, and the type of building—commercial, residential, or administrative. In contrast, the citizen must make a trade-off between the building's environmental impact, comfort levels, and energy use. In this essay, we'll suggest a smart model that enables management, control, and regulation of energy usage in accordance with a set of standards. As a result, this approach enables real-time calculation, regulation, and optimization of energy usage as well as comfort for the occupants. As a result, the person can learn about their energy consumption without having to read electricity measurements or wait for a billing cycle. Additionally, this method enables energy resource conservation and increases system output even during periods of high demand.

Experimental setup implements the concept of degree of observability (DoO) adequate a land-vehicle navigation application with noised inertial measurement unit (IMU) and global positioning system (GPS) sensors based on a loosely coupled approach. The navigation systems such as IMU-GPS require extensive evaluations of nonlinear equations as used in an extended Kalman filter (EKF). According to DoO and during our test, we have implemented a method for measuring the DoO of all states continuously. Where, the results showed that applying the fusion IMU-GPS system based on EKF be enhanced the DoO measure. The real dataset consists of outputs a high sampling rate for IMU sensor at each (0.01s) and GPS receiver at each (1s). In addition, an aloft category IMU was put together with differential GPS (DGPS) information to produce a real trajectory. GPS has acceptable long-term accuracy, it is used to update the position and velocity in IMU outputs before processing in the EKF algorithm. The implementation consists of three main algorithms: Strapdown (dead reckoning DR), DoO and EKF algorithms. The results are shown, implementation of both approaches based on EKF and the concept of DoO in GPS/INS integrated systems are sufficient robustness to use with low-cost sensors.

Among the most serious natural disasters, earthquakes cause severe damages to infrastructures and building, can kill or injure thousands of humans and animals and, in the luckiest circumstances, just make people homeless destroying communities, habitats, economies and mental equilibrium. In order to minimise the loss of lives, an effective evacuation plan to cope with worldwide disasters is required. In this paper we describe a novel approach to timely formulate an evacuation plan of an area struck by an earthquake. The proposed solution leverages on a two-steps modeling framework: i) a method that extracts from enriched GIS data a network description of the area to be evacuated; ii) a dynamic optimization model that calculates the safest paths citizens should follow to reach pre-identified safe areas. While the network is computed off-line at design time, the optimization model, or one of its reductions, can be embedded in a real-time system that, recomputing it several times, can guide citizen after a natural disaster even in case of high dynamic scenario. Our approach is demonstrated on a real study case: the medieval center of the Italian town of Sulmona, for which detailed GIS data with information on the urban structure and building vulnerability are available.

Natural disasters can cause widespread damage to buildings and infrastructures and kill thousands of living beings. These events are difficult to be overcome both by the populations and by government authorities. Two challenging issues require in particular to be addressed: find an effective way to evacuate people first, and later to rebuild houses and other infrastructures. An adequate recovery strategy to evacuate people and start reconstructing damaged areas on a priority basis can then be a game changer allowing to overcome effectively those terrible circumstances. In this perspective, we here present DiReCT, an approach based on i) a dynamic optimization model designed to timely formulate an evacuation plan of an area struck by an earthquake, and ii) a decision support system, based on double deep Q Network, able to guide efficiently the reconstruction the affected areas. The latter works by considering both the resources available and the needs of the various stakeholders involved (e.g., residents social benefits and political priorities). The ground on which both the above solutions stand was a dedicated geographical data extraction algorithm, called “GisToGraph”, especially developed for this purpose. To check applicability of the whole approach, we dovetailed it on the real use-case of the historical city center of L’Aquila (Italy) using detailed GIS data and information on urban land structure and buildings vulnerability. Several simulations were run on the underlining network generated. First, we ran experiments to safely evacuate in the shortest possible time as many people as possible from an endangered area towards a set of safe places. Then, using DDQN, we generated different reconstruction plans and selected the best ones considering both social benefits and political priorities of the building units. The described approaches are comprised in a more general data science framework delved to produce an effective response to natural disasters.

In GIS, spatial analysis is based on the use of spatial operations such as testing the spatial relations between features. Often, such tests are invalidated by errors in datasets. It is a very common experience that two bordering regions which should obey the topological relation “meet” fall instead in the “overlap” category. The situation is exacerbated when applying topological operators to regions that come from different datasets, where resolution and error sources are different. Despite the problem being quite common, up to now no standard approach has been defined to deal with spatial relations affected by errors of various origins. Referring to topological relations, we define a model to extend the eight Egenhofer relations between two simple regions: we call them homological relations (H‐relations). We discuss how exact topological relations can be extracted from observed relations and discuss the case of irregular tessellations, where errors have the most impact on vector data. In the proposed case study within the domain of geographic crowdsourced data, we propose algorithms for identifying homological regions and obtaining a corrected tessellation. This methodology can be considered as a step for quality control and the certification of irregular tessellations.

Navigation systems provide help to moving agents by enabling them to reach a desired destination. The development of the Global Positioning System (GPS) has enabled the development of outdoor navigation systems, which are based on the knowledge of a map and the user's location and provide guidance indications. The reality of indoor navigation systems is much different: difficulties stem from the fact that the movement of users is not constrained to belong to a well-established network such as the road network in the case of cars, but it is generally freer and less codifiable. This paper focuses on the automatic construction of a navigation graph superimposed on the map of a building that describes the possibilities of movement of a user within the building itself. The construction of the graph tries to replicate the spontaneous movement of users.

This book aims at promoting new and innovative studies, proposing new architectures or innovative evolutions of existing ones, and illustrating experiments on current technologies in order to improve the efficiency and effectiveness of distributed and cluster systems when they deal with spatiotemporal data.

Abstract. This paper discusses the generation of routing instructions in indoor space. We develop a user-friendly application that guides a pedestrian to reach a desired destination within a typical building floor in a simply and quickly way. Starting from a suitable navigation graph of the floor, the application considers qualitative reasoning techniques to produce indications such as “go slightly further” or “keep on the right” and adds a semantic level that provides indications on the visible landmarks along the route, such as “continue after the reception desk on the right”. This characteristic is fundamental when the users do not have a previous knowledge of the building, as they can be reassured by the presence of landmarks that help to understand if the taken direction is the correct one. The algorithm proposed on this paper is able to generate navigation instructions closer to what would be the typical indications of a human guide. We evaluate the result for some case studies of building floors.

Geographic data analysis is based on the use of spatial relations as a means of selecting and processing geometric data associated with geographic features. Starting from 1990, topological relations have been recognized as fundamental criteria in geographic data processing, leaving out other kinds of spatial relations, such as directional relations. The latter ones, despite having quite an important role in geospatial applications, have been developed as theoretical models but very little implemented in systems. We refer in this paper to the 5-intersection model for expressing projective relations that can be used to implement directional relations in various frames of reference. We design an application framework in Java and use the framework for answering various categories of queries involving directions. We finally outline how to use the framework for validating the cognitive adequacy of relations with user tests.

Crowdsourcing geographic information has been a massive phenomenon that look place over the last two decades and that changed dramatically the diffusion of geographic data in society. The availability and readiness of geographic information is paramount in practically all software systems and services and this is strongly determined by the drive of people putting energy in data collection and improvement. Understanding the motivations of people to participate in this process is fundamental to leverage the design of new systems able to increase satisfaction and productivity. The introduction of game mechanisms in geographic crowdsourcing is the theme that we face in this research. The so-called gamification of applications has been widely used as a stimulant for involving people in serious activities that are hidden behind the gaming facade. Cooperation and competition are key factors that are promoted through the game. We synthesize a conceptual model that collects the main gaming techniques tailored for geographic data and that is the basis for the development of a gamification library. As a proof of concept, we deploy an Android app for geometric data collection inside buildings and test it in a university setting.

Die digitalen Stadtpläne vieler Städte sind ungenau bzw. unvollständig. Ein An-satz wäre es, mit freiwillig zur Verfügung gestellten geographischen Daten (VGI, Volunteered Geographic Information) Städte qualitativ hochwertig und dennoch günstig zu kartieren. Dieser Ansatz wurde in der Stadt L’Aquila in Italien erprobt. Als Kartierungsplattform wurde OpenStreetMap (OSM) ge-wählt. Es bezieht seine Stärke aus der Zusammenarbeit von tausenden freiwilligen Kontributoren. Zu-gleich liegt hier auch seine größte Schwäche. Im Gegensatz zu zentral gelenkten Kartenwerken besitzt OSM kein korrigierendes Organ für inkonsistente Darstellungen von gleichartigen Details. Dieses Sig-naturenproblem und ein Ansatz für eine mögliche Lösung wird gezeigt und diskutiert.

We propose a navigation model for indoor environments that combines a 3D geometric modeling of buildings with connection properties of spaces and semantic elements such as openings and installations. The model is an extension of the IndoorGML standard navigation module with a twofold benefit: the extension facilitated the data import from the international standard CityGML and introduced the semantics of various fixtures in indoor space of buildings making the navigation model more suitable for human needs. Several experiments have been conducted by extracting networks from CityGML data and performing a comparison with other network construction techniques. The second contribution of the paper is an algorithm for the automatic extraction of the navigation network. Such an algorithm is a hybrid solution between medial axis approaches and visibility graph approaches. Normally, medial axes approaches are a good representation of human navigation in narrow corridors, especially to avoid obstacles, but introduce distortions in open space. On the other hand, visibility approaches work better in open spaces. In our extraction technique, the resulting network takes advantages of both approaches and better mimics human beings' navigation in indoor environments.

Land cover units are aggregations of land cover components that are obtained by using criteria of homogeneity and proximity of basic components. For example, residential urban settlements can be defined as aggregations of single buildings, neighboring green spaces, paved surfaces and small roads, which are separated by more prominent land cover components, such as main roads or rivers. Land cover components belong to standard classes typically obtained by an automated classification process applied to aerial or satellite images, such as buildings, constructed areas, bare soil, water, vegetation, and the like. Land cover units belong to more general classes, obtained by a combination of land cover components, such as residential areas, industrial areas, road networks, river systems, and agricultural units. In this paper, we describe an approach based on the application of geometric rules and semantic constraints to extract land cover units from land cover components. We use spatial operators to extract composite land cover units from land cover databases, where spatial operators are taken from standards of the Open Geospatial Consortium. Expert knowledge needs to be translated into specific automatic procedures, called complex object definitions or CODs. Finally, we build a prototype system, where the user can choose among a set of available CODs to build a sequence of actions that lead to the discovery of knowledge. We discuss several study cases, such as the recognition of urban settlements, agricultural land units, and road networks.

Various approaches lie behind the modelling of spatial relations, which is a heterogeneous and interdisciplinary field. In this paper, we introduce a conceptual framework to describe the characteristics of various models and how they relate each other. A first categorization is made among three representation levels: geometric, computational, and user. At the geometric level, spatial objects can be seen as point-sets and relations can be formally defined at the mathematical level. At the computational level, objects are represented as data types and relations are computed via spatial operators. At the user level, objects and relations belong to a context-dependent user ontology. Another way of providing a categorization is following the underlying geometric space that describes the relations: we distinguish among topologic, projective, and metric relations. Then, we consider the cardinality of spatial relations, which is defined as the number of objects that participate in the relation. Another issue is the granularity at which the relation is described, ranging from general descriptions to very detailed ones. We also consider the dimension of the various geometric objects and the embedding space as a fundamental way of categorizing relations.